Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations37765
Missing cells70824
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 MiB
Average record size in memory144.0 B

Variable types

Numeric11
Text4
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
price has 14721 (39.0%) missing valuesMissing
last_review has 11751 (31.1%) missing valuesMissing
reviews_per_month has 11751 (31.1%) missing valuesMissing
license has 32594 (86.3%) missing valuesMissing
price is highly skewed (γ1 = 99.07291859)Skewed
number_of_reviews_ltm is highly skewed (γ1 = 40.57284443)Skewed
id has unique valuesUnique
number_of_reviews has 11751 (31.1%) zerosZeros
availability_365 has 12546 (33.2%) zerosZeros
number_of_reviews_ltm has 23090 (61.1%) zerosZeros

Reproduction

Analysis started2024-09-14 23:42:00.299602
Analysis finished2024-09-14 23:42:06.205034
Duration5.91 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct37765
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6530375 × 1017
Minimum2595
Maximum1.1930861 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:06.247990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2595
5-th percentile3468623
Q120552544
median48263817
Q38.2763805 × 1017
95-th percentile1.1282097 × 1018
Maximum1.1930861 × 1018
Range1.1930861 × 1018
Interquartile range (IQR)8.2763805 × 1017

Descriptive statistics

Standard deviation4.5399076 × 1017
Coefficient of variation (CV)1.2427761
Kurtosis-1.467409
Mean3.6530375 × 1017
Median Absolute Deviation (MAD)41674870
Skewness0.55970973
Sum-2.4684305 × 1018
Variance2.0610761 × 1035
MonotonicityStrictly increasing
2024-09-14T19:42:06.300614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2595 1
 
< 0.1%
7.006563344 × 10171
 
< 0.1%
6.998975458 × 10171
 
< 0.1%
6.999004723 × 10171
 
< 0.1%
6.999140631 × 10171
 
< 0.1%
7.000008124 × 10171
 
< 0.1%
7.000031192 × 10171
 
< 0.1%
7.000647756 × 10171
 
< 0.1%
7.000884189 × 10171
 
< 0.1%
7.001112691 × 10171
 
< 0.1%
Other values (37755) 37755
> 99.9%
ValueCountFrequency (%)
2595 1
< 0.1%
5136 1
< 0.1%
6848 1
< 0.1%
6872 1
< 0.1%
6990 1
< 0.1%
7064 1
< 0.1%
7097 1
< 0.1%
7801 1
< 0.1%
8490 1
< 0.1%
9357 1
< 0.1%
ValueCountFrequency (%)
1.193086059 × 10181
< 0.1%
1.193068036 × 10181
< 0.1%
1.19301613 × 10181
< 0.1%
1.193005543 × 10181
< 0.1%
1.192946306 × 10181
< 0.1%
1.192903502 × 10181
< 0.1%
1.192899751 × 10181
< 0.1%
1.192828516 × 10181
< 0.1%
1.192788528 × 10181
< 0.1%
1.192723875 × 10181
< 0.1%

name
Text

Distinct36050
Distinct (%)95.5%
Missing2
Missing (%)< 0.1%
Memory size295.2 KiB
2024-09-14T19:42:06.455804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length245
Median length61
Mean length36.817123
Min length1

Characters and Unicode

Total characters1390325
Distinct characters718
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35175 ?
Unique (%)93.1%

Sample

1st rowSkylit Midtown Castle
2nd rowSpacious Family Friendly Duplex w/ Patio + Yard
3rd rowOnly 2 stops to Manhattan studio
4th rowUptown Sanctuary w/ Private Bath (Month to Month)
5th rowUES Beautiful Blue Room
ValueCountFrequency (%)
in 11248
 
4.8%
room 7829
 
3.4%
7481
 
3.2%
private 5376
 
2.3%
bedroom 5297
 
2.3%
apartment 4432
 
1.9%
cozy 3250
 
1.4%
studio 3171
 
1.4%
apt 2930
 
1.3%
to 2920
 
1.3%
Other values (11678) 178075
76.8%
2024-09-14T19:42:06.675270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195249
 
14.0%
e 101097
 
7.3%
o 96181
 
6.9%
t 80884
 
5.8%
a 78776
 
5.7%
r 76154
 
5.5%
n 73050
 
5.3%
i 72943
 
5.2%
l 40348
 
2.9%
s 37983
 
2.7%
Other values (708) 537660
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1390325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
195249
 
14.0%
e 101097
 
7.3%
o 96181
 
6.9%
t 80884
 
5.8%
a 78776
 
5.7%
r 76154
 
5.5%
n 73050
 
5.3%
i 72943
 
5.2%
l 40348
 
2.9%
s 37983
 
2.7%
Other values (708) 537660
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1390325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
195249
 
14.0%
e 101097
 
7.3%
o 96181
 
6.9%
t 80884
 
5.8%
a 78776
 
5.7%
r 76154
 
5.5%
n 73050
 
5.3%
i 72943
 
5.2%
l 40348
 
2.9%
s 37983
 
2.7%
Other values (708) 537660
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1390325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
195249
 
14.0%
e 101097
 
7.3%
o 96181
 
6.9%
t 80884
 
5.8%
a 78776
 
5.7%
r 76154
 
5.5%
n 73050
 
5.3%
i 72943
 
5.2%
l 40348
 
2.9%
s 37983
 
2.7%
Other values (708) 537660
38.7%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct22663
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6596842 × 108
Minimum1678
Maximum5.8691743 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:06.746372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1678
5-th percentile1472922
Q116627758
median82189528
Q33.0315693 × 108
95-th percentile5.1260944 × 108
Maximum5.8691743 × 108
Range5.8691575 × 108
Interquartile range (IQR)2.8652917 × 108

Descriptive statistics

Standard deviation1.8005294 × 108
Coefficient of variation (CV)1.0848626
Kurtosis-0.67312507
Mean1.6596842 × 108
Median Absolute Deviation (MAD)78457267
Skewness0.87760849
Sum6.2677973 × 1012
Variance3.241906 × 1016
MonotonicityNot monotonic
2024-09-14T19:42:06.798054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 842
 
2.2%
3223938 692
 
1.8%
305240193 351
 
0.9%
446820235 329
 
0.9%
19303369 262
 
0.7%
200239515 246
 
0.7%
501999278 245
 
0.6%
204704622 226
 
0.6%
162280872 212
 
0.6%
501499086 170
 
0.5%
Other values (22653) 34190
90.5%
ValueCountFrequency (%)
1678 1
 
< 0.1%
2234 1
 
< 0.1%
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 3
< 0.1%
2845 3
< 0.1%
2868 1
 
< 0.1%
3869 1
 
< 0.1%
4396 2
< 0.1%
5089 1
 
< 0.1%
ValueCountFrequency (%)
586917430 2
< 0.1%
586900449 1
 
< 0.1%
586895343 1
 
< 0.1%
586836710 3
< 0.1%
586444653 1
 
< 0.1%
586424140 1
 
< 0.1%
586339781 1
 
< 0.1%
586297193 1
 
< 0.1%
585984692 1
 
< 0.1%
585973759 1
 
< 0.1%
Distinct8534
Distinct (%)22.6%
Missing5
Missing (%)< 0.1%
Memory size295.2 KiB
2024-09-14T19:42:06.929617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length6.8542638
Min length1

Characters and Unicode

Total characters258817
Distinct characters140
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4967 ?
Unique (%)13.2%

Sample

1st rowJennifer
2nd rowRebecca
3rd rowAllen & Irina
4th rowKae
5th rowCyn
ValueCountFrequency (%)
blueground 842
 
1.9%
eugene 698
 
1.6%
roompicks 498
 
1.1%
furnished 401
 
0.9%
elena 390
 
0.9%
michael 368
 
0.8%
and 358
 
0.8%
kristina 354
 
0.8%
hotel 330
 
0.8%
luxurybookingsfze 329
 
0.8%
Other values (8060) 39149
89.6%
2024-09-14T19:42:07.122791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 27528
 
10.6%
e 24449
 
9.4%
n 20030
 
7.7%
i 19809
 
7.7%
r 15310
 
5.9%
o 14266
 
5.5%
l 12504
 
4.8%
s 9269
 
3.6%
t 8407
 
3.2%
u 8345
 
3.2%
Other values (130) 98900
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27528
 
10.6%
e 24449
 
9.4%
n 20030
 
7.7%
i 19809
 
7.7%
r 15310
 
5.9%
o 14266
 
5.5%
l 12504
 
4.8%
s 9269
 
3.6%
t 8407
 
3.2%
u 8345
 
3.2%
Other values (130) 98900
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27528
 
10.6%
e 24449
 
9.4%
n 20030
 
7.7%
i 19809
 
7.7%
r 15310
 
5.9%
o 14266
 
5.5%
l 12504
 
4.8%
s 9269
 
3.6%
t 8407
 
3.2%
u 8345
 
3.2%
Other values (130) 98900
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27528
 
10.6%
e 24449
 
9.4%
n 20030
 
7.7%
i 19809
 
7.7%
r 15310
 
5.9%
o 14266
 
5.5%
l 12504
 
4.8%
s 9269
 
3.6%
t 8407
 
3.2%
u 8345
 
3.2%
Other values (130) 98900
38.2%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.2 KiB
Manhattan
16715 
Brooklyn
13885 
Queens
5579 
Bronx
 
1227
Staten Island
 
359

Length

Max length13
Median length9
Mean length8.0972064
Min length5

Characters and Unicode

Total characters305791
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManhattan
2nd rowBrooklyn
3rd rowBrooklyn
4th rowManhattan
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 16715
44.3%
Brooklyn 13885
36.8%
Queens 5579
 
14.8%
Bronx 1227
 
3.2%
Staten Island 359
 
1.0%

Length

2024-09-14T19:42:07.283495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T19:42:07.331677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 16715
43.8%
brooklyn 13885
36.4%
queens 5579
 
14.6%
bronx 1227
 
3.2%
staten 359
 
0.9%
island 359
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 54839
17.9%
a 50863
16.6%
t 34148
11.2%
o 28997
9.5%
M 16715
 
5.5%
h 16715
 
5.5%
B 15112
 
4.9%
r 15112
 
4.9%
l 14244
 
4.7%
y 13885
 
4.5%
Other values (10) 45161
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 305791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 54839
17.9%
a 50863
16.6%
t 34148
11.2%
o 28997
9.5%
M 16715
 
5.5%
h 16715
 
5.5%
B 15112
 
4.9%
r 15112
 
4.9%
l 14244
 
4.7%
y 13885
 
4.5%
Other values (10) 45161
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 305791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 54839
17.9%
a 50863
16.6%
t 34148
11.2%
o 28997
9.5%
M 16715
 
5.5%
h 16715
 
5.5%
B 15112
 
4.9%
r 15112
 
4.9%
l 14244
 
4.7%
y 13885
 
4.5%
Other values (10) 45161
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 305791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 54839
17.9%
a 50863
16.6%
t 34148
11.2%
o 28997
9.5%
M 16715
 
5.5%
h 16715
 
5.5%
B 15112
 
4.9%
r 15112
 
4.9%
l 14244
 
4.7%
y 13885
 
4.5%
Other values (10) 45161
14.8%
Distinct225
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:07.453587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length18
Mean length11.788852
Min length4

Characters and Unicode

Total characters445206
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowMidtown
2nd rowSunset Park
3rd rowWilliamsburg
4th rowEast Harlem
5th rowEast Harlem
ValueCountFrequency (%)
east 4828
 
7.9%
side 3605
 
5.9%
upper 2992
 
4.9%
bedford-stuyvesant 2709
 
4.4%
heights 2600
 
4.2%
harlem 2467
 
4.0%
williamsburg 2206
 
3.6%
west 2044
 
3.3%
midtown 2028
 
3.3%
village 1925
 
3.1%
Other values (236) 33973
55.4%
2024-09-14T19:42:07.631964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 40581
 
9.1%
i 31904
 
7.2%
t 30777
 
6.9%
s 30140
 
6.8%
a 28615
 
6.4%
r 25285
 
5.7%
l 24743
 
5.6%
23612
 
5.3%
n 20714
 
4.7%
o 19275
 
4.3%
Other values (44) 169560
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 445206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 40581
 
9.1%
i 31904
 
7.2%
t 30777
 
6.9%
s 30140
 
6.8%
a 28615
 
6.4%
r 25285
 
5.7%
l 24743
 
5.6%
23612
 
5.3%
n 20714
 
4.7%
o 19275
 
4.3%
Other values (44) 169560
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 445206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 40581
 
9.1%
i 31904
 
7.2%
t 30777
 
6.9%
s 30140
 
6.8%
a 28615
 
6.4%
r 25285
 
5.7%
l 24743
 
5.6%
23612
 
5.3%
n 20714
 
4.7%
o 19275
 
4.3%
Other values (44) 169560
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 445206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 40581
 
9.1%
i 31904
 
7.2%
t 30777
 
6.9%
s 30140
 
6.8%
a 28615
 
6.4%
r 25285
 
5.7%
l 24743
 
5.6%
23612
 
5.3%
n 20714
 
4.7%
o 19275
 
4.3%
Other values (44) 169560
38.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct23546
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.729075
Minimum40.500366
Maximum40.91139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:07.698591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40.500366
5-th percentile40.641248
Q140.6888
median40.72644
Q340.76247
95-th percentile40.826338
Maximum40.91139
Range0.4110237
Interquartile range (IQR)0.07367

Descriptive statistics

Standard deviation0.05634298
Coefficient of variation (CV)0.0013833602
Kurtosis0.1803726
Mean40.729075
Median Absolute Deviation (MAD)0.036892
Skewness0.15110474
Sum1538133.5
Variance0.0031745314
MonotonicityNot monotonic
2024-09-14T19:42:07.747981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76153 54
 
0.1%
40.61881 46
 
0.1%
40.8024986 37
 
0.1%
40.7607614 36
 
0.1%
40.76411 28
 
0.1%
40.7092191 27
 
0.1%
40.7526556 25
 
0.1%
40.7456565 23
 
0.1%
40.6789456 23
 
0.1%
40.7056 23
 
0.1%
Other values (23536) 37443
99.1%
ValueCountFrequency (%)
40.5003663 1
< 0.1%
40.50456 1
< 0.1%
40.507114 1
< 0.1%
40.50863 1
< 0.1%
40.51707 1
< 0.1%
40.52034 1
< 0.1%
40.52224 1
< 0.1%
40.52339 1
< 0.1%
40.52442553 1
< 0.1%
40.52498 1
< 0.1%
ValueCountFrequency (%)
40.91139 1
< 0.1%
40.91138 1
< 0.1%
40.91114684 1
< 0.1%
40.91062 1
< 0.1%
40.90667 1
< 0.1%
40.906568 1
< 0.1%
40.90549 1
< 0.1%
40.90530196 1
< 0.1%
40.90505 1
< 0.1%
40.90421 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct21266
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.947084
Minimum-74.251907
Maximum-73.71365
Zeros0
Zeros (%)0.0%
Negative37765
Negative (%)100.0%
Memory size295.2 KiB
2024-09-14T19:42:07.796004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-74.251907
5-th percentile-74.004718
Q1-73.98322
median-73.95454
Q3-73.928022
95-th percentile-73.832298
Maximum-73.71365
Range0.538257
Interquartile range (IQR)0.05519804

Descriptive statistics

Standard deviation0.054457768
Coefficient of variation (CV)-0.0007364424
Kurtosis3.5505265
Mean-73.947084
Median Absolute Deviation (MAD)0.0280055
Skewness1.2156205
Sum-2792611.6
Variance0.0029656485
MonotonicityNot monotonic
2024-09-14T19:42:07.847829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99878 53
 
0.1%
-73.9668444 37
 
0.1%
-73.9861055 36
 
0.1%
-74.0323824 31
 
0.1%
-73.99371 29
 
0.1%
-74.0137534 27
 
0.1%
-73.9724804 25
 
0.1%
-73.93061 24
 
0.1%
-73.9070911 23
 
0.1%
-73.9437591 23
 
0.1%
Other values (21256) 37457
99.2%
ValueCountFrequency (%)
-74.251907 1
< 0.1%
-74.24984 1
< 0.1%
-74.24135 1
< 0.1%
-74.2394006 1
< 0.1%
-74.22263763 1
< 0.1%
-74.21514 1
< 0.1%
-74.20739 1
< 0.1%
-74.20517 1
< 0.1%
-74.20516 1
< 0.1%
-74.20178157 1
< 0.1%
ValueCountFrequency (%)
-73.71365 1
< 0.1%
-73.71383551 1
< 0.1%
-73.7174099 1
< 0.1%
-73.7198118 1
< 0.1%
-73.71991 1
< 0.1%
-73.72408 1
< 0.1%
-73.72434 1
< 0.1%
-73.72543 1
< 0.1%
-73.72606 1
< 0.1%
-73.72614 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.2 KiB
Entire home/apt
20023 
Private room
16645 
Hotel room
 
560
Shared room
 
537

Length

Max length15
Median length15
Mean length13.546723
Min length10

Characters and Unicode

Total characters511592
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 20023
53.0%
Private room 16645
44.1%
Hotel room 560
 
1.5%
Shared room 537
 
1.4%

Length

2024-09-14T19:42:07.891762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-14T19:42:07.930109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 20023
26.5%
home/apt 20023
26.5%
room 17742
23.5%
private 16645
22.0%
hotel 560
 
0.7%
shared 537
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 57788
11.3%
t 57251
11.2%
o 56067
11.0%
r 54947
10.7%
m 37765
 
7.4%
37765
 
7.4%
a 37205
 
7.3%
i 36668
 
7.2%
h 20560
 
4.0%
p 20023
 
3.9%
Other values (9) 95553
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 511592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57788
11.3%
t 57251
11.2%
o 56067
11.0%
r 54947
10.7%
m 37765
 
7.4%
37765
 
7.4%
a 37205
 
7.3%
i 36668
 
7.2%
h 20560
 
4.0%
p 20023
 
3.9%
Other values (9) 95553
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 511592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57788
11.3%
t 57251
11.2%
o 56067
11.0%
r 54947
10.7%
m 37765
 
7.4%
37765
 
7.4%
a 37205
 
7.3%
i 36668
 
7.2%
h 20560
 
4.0%
p 20023
 
3.9%
Other values (9) 95553
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 511592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57788
11.3%
t 57251
11.2%
o 56067
11.0%
r 54947
10.7%
m 37765
 
7.4%
37765
 
7.4%
a 37205
 
7.3%
i 36668
 
7.2%
h 20560
 
4.0%
p 20023
 
3.9%
Other values (9) 95553
18.7%

price
Real number (ℝ)

MISSING  SKEWED 

Distinct982
Distinct (%)4.3%
Missing14721
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean221.0749
Minimum8
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:07.975743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile44
Q186
median150
Q3250
95-th percentile550
Maximum100000
Range99992
Interquartile range (IQR)164

Descriptive statistics

Standard deviation766.62099
Coefficient of variation (CV)3.467698
Kurtosis12524.338
Mean221.0749
Median Absolute Deviation (MAD)75
Skewness99.072919
Sum5094450
Variance587707.75
MonotonicityNot monotonic
2024-09-14T19:42:08.029877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 535
 
1.4%
200 441
 
1.2%
100 421
 
1.1%
120 346
 
0.9%
250 329
 
0.9%
125 303
 
0.8%
60 291
 
0.8%
80 289
 
0.8%
90 280
 
0.7%
70 265
 
0.7%
Other values (972) 19544
51.8%
(Missing) 14721
39.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 9
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
22 2
 
< 0.1%
23 2
 
< 0.1%
24 2
 
< 0.1%
25 3
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
20000 3
< 0.1%
12452 1
 
< 0.1%
10799 1
 
< 0.1%
10000 4
< 0.1%
9999 1
 
< 0.1%
8550 1
 
< 0.1%
8000 1
 
< 0.1%
7600 1
 
< 0.1%
7500 1
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct118
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.164597
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.076427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q130
median30
Q330
95-th percentile31
Maximum1250
Range1249
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.202538
Coefficient of variation (CV)1.0355891
Kurtosis415.97221
Mean29.164597
Median Absolute Deviation (MAD)0
Skewness16.189945
Sum1101401
Variance912.19328
MonotonicityNot monotonic
2024-09-14T19:42:08.133466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 30448
80.6%
1 2913
 
7.7%
31 1190
 
3.2%
2 865
 
2.3%
3 653
 
1.7%
90 336
 
0.9%
4 216
 
0.6%
60 198
 
0.5%
5 195
 
0.5%
7 92
 
0.2%
Other values (108) 659
 
1.7%
ValueCountFrequency (%)
1 2913
7.7%
2 865
 
2.3%
3 653
 
1.7%
4 216
 
0.6%
5 195
 
0.5%
6 31
 
0.1%
7 92
 
0.2%
8 1
 
< 0.1%
9 3
 
< 0.1%
10 9
 
< 0.1%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1124 1
 
< 0.1%
1000 6
< 0.1%
999 1
 
< 0.1%
729 6
< 0.1%
700 1
 
< 0.1%
500 6
< 0.1%
480 1
 
< 0.1%
400 3
< 0.1%
370 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct475
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.898981
Minimum0
Maximum1915
Zeros11751
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.182432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile128
Maximum1915
Range1915
Interquartile range (IQR)21

Descriptive statistics

Standard deviation58.802122
Coefficient of variation (CV)2.3616277
Kurtosis94.912428
Mean24.898981
Median Absolute Deviation (MAD)3
Skewness6.5506417
Sum940310
Variance3457.6895
MonotonicityNot monotonic
2024-09-14T19:42:08.236800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11751
31.1%
1 3586
 
9.5%
2 2195
 
5.8%
3 1548
 
4.1%
4 1216
 
3.2%
5 973
 
2.6%
6 848
 
2.2%
7 761
 
2.0%
8 677
 
1.8%
9 592
 
1.6%
Other values (465) 13618
36.1%
ValueCountFrequency (%)
0 11751
31.1%
1 3586
 
9.5%
2 2195
 
5.8%
3 1548
 
4.1%
4 1216
 
3.2%
5 973
 
2.6%
6 848
 
2.2%
7 761
 
2.0%
8 677
 
1.8%
9 592
 
1.6%
ValueCountFrequency (%)
1915 1
< 0.1%
1584 1
< 0.1%
1551 1
< 0.1%
1316 1
< 0.1%
1294 1
< 0.1%
1177 1
< 0.1%
979 1
< 0.1%
964 1
< 0.1%
927 1
< 0.1%
764 1
< 0.1%

last_review
Date

MISSING 

Distinct3059
Distinct (%)11.8%
Missing11751
Missing (%)31.1%
Memory size295.2 KiB
Minimum2011-05-12 00:00:00
Maximum2024-07-05 00:00:00
2024-09-14T19:42:08.335729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:08.387371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct788
Distinct (%)3.0%
Missing11751
Missing (%)31.1%
Infinite0
Infinite (%)0.0%
Mean0.90554394
Minimum0.01
Maximum103.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.436813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.09
median0.32
Q31.13
95-th percentile3.38
Maximum103.53
Range103.52
Interquartile range (IQR)1.04

Descriptive statistics

Standard deviation1.8607836
Coefficient of variation (CV)2.0548795
Kurtosis749.32242
Mean0.90554394
Median Absolute Deviation (MAD)0.28
Skewness18.49558
Sum23556.82
Variance3.4625158
MonotonicityNot monotonic
2024-09-14T19:42:08.500667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 1081
 
2.9%
0.02 986
 
2.6%
0.03 847
 
2.2%
0.04 693
 
1.8%
0.05 686
 
1.8%
0.06 606
 
1.6%
0.07 542
 
1.4%
0.09 542
 
1.4%
0.08 524
 
1.4%
0.1 472
 
1.2%
Other values (778) 19035
50.4%
(Missing) 11751
31.1%
ValueCountFrequency (%)
0.01 1081
2.9%
0.02 986
2.6%
0.03 847
2.2%
0.04 693
1.8%
0.05 686
1.8%
0.06 606
1.6%
0.07 542
1.4%
0.08 524
1.4%
0.09 542
1.4%
0.1 472
1.2%
ValueCountFrequency (%)
103.53 1
< 0.1%
96.75 1
< 0.1%
57.05 1
< 0.1%
47 1
< 0.1%
45.35 1
< 0.1%
44.31 1
< 0.1%
42.44 1
< 0.1%
38.46 1
< 0.1%
35 1
< 0.1%
34.21 1
< 0.1%
Distinct73
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.763035
Minimum1
Maximum842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.547454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile329
Maximum842
Range841
Interquartile range (IQR)7

Descriptive statistics

Standard deviation161.97972
Coefficient of variation (CV)3.0128456
Kurtosis14.168405
Mean53.763035
Median Absolute Deviation (MAD)1
Skewness3.8170847
Sum2030361
Variance26237.431
MonotonicityNot monotonic
2024-09-14T19:42:08.596940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18880
50.0%
2 4330
 
11.5%
3 2040
 
5.4%
4 1308
 
3.5%
842 842
 
2.2%
5 730
 
1.9%
692 692
 
1.8%
6 600
 
1.6%
8 448
 
1.2%
7 406
 
1.1%
Other values (63) 7489
 
19.8%
ValueCountFrequency (%)
1 18880
50.0%
2 4330
 
11.5%
3 2040
 
5.4%
4 1308
 
3.5%
5 730
 
1.9%
6 600
 
1.6%
7 406
 
1.1%
8 448
 
1.2%
9 369
 
1.0%
10 330
 
0.9%
ValueCountFrequency (%)
842 842
2.2%
692 692
1.8%
351 351
0.9%
329 329
 
0.9%
262 262
 
0.7%
246 246
 
0.7%
245 245
 
0.6%
226 226
 
0.6%
212 212
 
0.6%
170 170
 
0.5%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.4116
Minimum0
Maximum365
Zeros12546
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.641968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126
Q3301
95-th percentile364
Maximum365
Range365
Interquartile range (IQR)301

Descriptive statistics

Standard deviation142.79733
Coefficient of variation (CV)0.93691907
Kurtosis-1.5724648
Mean152.4116
Median Absolute Deviation (MAD)126
Skewness0.24675532
Sum5755824
Variance20391.078
MonotonicityNot monotonic
2024-09-14T19:42:08.695463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12546
33.2%
365 1784
 
4.7%
364 1359
 
3.6%
269 749
 
2.0%
363 674
 
1.8%
89 373
 
1.0%
270 351
 
0.9%
90 336
 
0.9%
179 316
 
0.8%
180 273
 
0.7%
Other values (356) 19004
50.3%
ValueCountFrequency (%)
0 12546
33.2%
1 129
 
0.3%
2 63
 
0.2%
3 35
 
0.1%
4 79
 
0.2%
5 65
 
0.2%
6 54
 
0.1%
7 43
 
0.1%
8 40
 
0.1%
9 28
 
0.1%
ValueCountFrequency (%)
365 1784
4.7%
364 1359
3.6%
363 674
 
1.8%
362 229
 
0.6%
361 51
 
0.1%
360 61
 
0.2%
359 99
 
0.3%
358 189
 
0.5%
357 77
 
0.2%
356 78
 
0.2%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct145
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9931683
Minimum0
Maximum1568
Zeros23090
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size295.2 KiB
2024-09-14T19:42:08.742315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile21
Maximum1568
Range1568
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.762591
Coefficient of variation (CV)3.9473897
Kurtosis3207.7332
Mean3.9931683
Median Absolute Deviation (MAD)0
Skewness40.572844
Sum150802
Variance248.45928
MonotonicityNot monotonic
2024-09-14T19:42:08.796166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23090
61.1%
1 3164
 
8.4%
2 1947
 
5.2%
3 1358
 
3.6%
4 1027
 
2.7%
5 797
 
2.1%
6 584
 
1.5%
7 477
 
1.3%
8 407
 
1.1%
9 380
 
1.0%
Other values (135) 4534
 
12.0%
ValueCountFrequency (%)
0 23090
61.1%
1 3164
 
8.4%
2 1947
 
5.2%
3 1358
 
3.6%
4 1027
 
2.7%
5 797
 
2.1%
6 584
 
1.5%
7 477
 
1.3%
8 407
 
1.1%
9 380
 
1.0%
ValueCountFrequency (%)
1568 1
< 0.1%
976 1
< 0.1%
742 1
< 0.1%
608 1
< 0.1%
526 1
< 0.1%
390 1
< 0.1%
343 1
< 0.1%
332 1
< 0.1%
331 1
< 0.1%
280 1
< 0.1%

license
Text

MISSING 

Distinct1732
Distinct (%)33.5%
Missing32594
Missing (%)86.3%
Memory size295.2 KiB
2024-09-14T19:42:08.902193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length6
Mean length11.335138
Min length6

Characters and Unicode

Total characters58614
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1449 ?
Unique (%)28.0%

Sample

1st rowOSE-STRREG-0000008
2nd rowOSE-STRREG-0000923
3rd rowOSE-STRREG-0000108
4th rowOSE-STRREG-0001710
5th rowOSE-STRREG-0000041
ValueCountFrequency (%)
exempt 2872
55.5%
ose-strreg-0000068 99
 
1.9%
ose-strreg-0041458 36
 
0.7%
ose-strreg-0001054 18
 
0.3%
ose-strreg-1081622 10
 
0.2%
ose-strreg-0091251 9
 
0.2%
ose-strreg-0001957 9
 
0.2%
ose-strreg-0000095 7
 
0.1%
ose-strreg-0000437 7
 
0.1%
ose-strreg-1044019 7
 
0.1%
Other values (1717) 2097
40.6%
2024-09-14T19:42:09.072419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8574
14.6%
E 7420
12.7%
- 4598
 
7.8%
S 4549
 
7.8%
R 4544
 
7.8%
e 2922
 
5.0%
t 2899
 
4.9%
m 2872
 
4.9%
p 2872
 
4.9%
x 2872
 
4.9%
Other values (16) 14492
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8574
14.6%
E 7420
12.7%
- 4598
 
7.8%
S 4549
 
7.8%
R 4544
 
7.8%
e 2922
 
5.0%
t 2899
 
4.9%
m 2872
 
4.9%
p 2872
 
4.9%
x 2872
 
4.9%
Other values (16) 14492
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8574
14.6%
E 7420
12.7%
- 4598
 
7.8%
S 4549
 
7.8%
R 4544
 
7.8%
e 2922
 
5.0%
t 2899
 
4.9%
m 2872
 
4.9%
p 2872
 
4.9%
x 2872
 
4.9%
Other values (16) 14492
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8574
14.6%
E 7420
12.7%
- 4598
 
7.8%
S 4549
 
7.8%
R 4544
 
7.8%
e 2922
 
5.0%
t 2899
 
4.9%
m 2872
 
4.9%
p 2872
 
4.9%
x 2872
 
4.9%
Other values (16) 14492
24.7%

Interactions

2024-09-14T19:42:05.464671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.284836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.861967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.257977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.627736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.078773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.449249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.827508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.264343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.636028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.059900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.548905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.327320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.901590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.290577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.662435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.111519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.483538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.862701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.298603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.673186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.100664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.584029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.418093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.935719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.323466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.696855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.144248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.517397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.899091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.331652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.710728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.136645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.618319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.452658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.969174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.355134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.730573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.176730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.550776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.932891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.364451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.744387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.170075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.654092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.501596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.002813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.387557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.762678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.209105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.583979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.968405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.397064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.777527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.204908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.689938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.555191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.040077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.423444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.796276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.241477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.617514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.002486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.429262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.809839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.239610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.726267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.618489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.077268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.459761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.905359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.273411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.650964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.038064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.465446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.844095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.278023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.762898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.707883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.113218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.495027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.941203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.312229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.687310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.074165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.500363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.879304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.314612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.798586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.750671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.147344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.527217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.974303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.346439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.720555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.109369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.533433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.913777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.356376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.837749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.786580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.185989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.558746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.008182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.379346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.754214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.191572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.565472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.950807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.392567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.882391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:01.825067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.221734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:02.593071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.043005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.413404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:03.790587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.228373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:04.600832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.012161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-14T19:42:05.428684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-14T19:42:09.131794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewsnumber_of_reviews_ltmpricereviews_per_monthroom_type
availability_3651.0000.4090.2410.422-0.0130.061-0.0400.0690.0310.2920.0650.3720.074
calculated_host_listings_count0.4091.0000.2540.4220.124-0.070-0.0510.166-0.2130.014-0.0270.1000.228
host_id0.2410.2541.0000.5420.0700.062-0.2330.119-0.1150.1290.0340.2790.191
id0.4220.4220.5421.0000.0240.022-0.1660.079-0.3500.1230.0800.3520.087
latitude-0.0130.1240.0700.0241.0000.001-0.0080.555-0.098-0.0700.102-0.0820.137
longitude0.061-0.0700.0620.0220.0011.0000.0360.6750.1070.105-0.4090.1250.149
minimum_nights-0.040-0.051-0.233-0.166-0.0080.0361.0000.016-0.226-0.358-0.139-0.4180.021
neighbourhood_group0.0690.1660.1190.0790.5550.6750.0161.0000.0210.0000.0030.0200.141
number_of_reviews0.031-0.213-0.115-0.350-0.0980.107-0.2260.0211.0000.660-0.0340.7830.029
number_of_reviews_ltm0.2920.0140.1290.123-0.0700.105-0.3580.0000.6601.0000.0120.7760.000
price0.065-0.0270.0340.0800.102-0.409-0.1390.003-0.0340.0121.0000.0670.000
reviews_per_month0.3720.1000.2790.352-0.0820.125-0.4180.0200.7830.7760.0671.0000.000
room_type0.0740.2280.1910.0870.1370.1490.0210.1410.0290.0000.0000.0001.000

Missing values

2024-09-14T19:42:05.946003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-14T19:42:06.029845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-14T19:42:06.168470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.753560-73.985590Entire home/aptNaN30492022-06-210.2833000NaN
15136Spacious Family Friendly Duplex w/ Patio + Yard7378RebeccaBrooklynSunset Park40.662650-73.994540Entire home/apt215.03042023-08-200.031711NaN
26848Only 2 stops to Manhattan studio15991Allen & IrinaBrooklynWilliamsburg40.709350-73.953420Entire home/apt81.0301932024-05-181.0511933NaN
36872Uptown Sanctuary w/ Private Bath (Month to Month)16104KaeManhattanEast Harlem40.801070-73.942550Private room65.03012022-06-050.0423650NaN
46990UES Beautiful Blue Room16800CynManhattanEast Harlem40.787780-73.947590Private room65.0302472024-03-061.3812122NaN
57064Amazing location! Wburg. Large, bright & tranquil17297JoelleBrooklynWilliamsburg40.712480-73.958810Private roomNaN30132022-09-120.08200NaN
67097Perfect for Your Parents, With Garden & Patio17571JaneBrooklynFort Greene40.691940-73.973890Private room205.023742024-06-022.12221936OSE-STRREG-0000008
77801Sunny Williamsburg Loft with Sauna21207ChayaBrooklynWilliamsburg40.718807-73.956177Entire home/apt290.030122023-10-310.0712192NaN
88490Maison des Sirenes1,bohemian, luminous apartment25183NathalieBrooklynBedford-Stuyvesant40.684556-73.939634Entire home/apt170.0301902023-10-161.0522157NaN
99357Midtown Pied-a-terre30193TommiManhattanHell's Kitchen40.767240-73.986640Entire home/apt175.030582017-08-130.3212810NaN
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
377551192723874961256642Classic Private Room #1361 C305240193ElenaManhattanLower East Side40.713986-73.984912Private room66.0300NaNNaN351340NaN
377561192788527851447904One-Bedroom Apartment in the Financial District22541573Furnished QuartersManhattanFinancial District40.707341-74.008599Entire home/apt340.0300NaNNaN1443290NaN
377571192828515848213902Barrow by Rove Travel | 1BR Loft w/Shared Roof17775359RoveManhattanWest Village40.731627-74.008061Entire home/apt337.0300NaNNaN783650NaN
377581192899750875253463Apollo by Rove Travel | 3BR Duplex w/Garden17775359RoveManhattanHarlem40.806637-73.953292Entire home/apt229.0300NaNNaN783510NaN
3775911929035018989230563 bed floor-through in Diplomatic District683230ThomasManhattanMidtown40.754207-73.966145Entire home/apt788.0300NaNNaN63390NaN
377601192946306020922085Private Studio in Brooklyn137493949SydneyBrooklynClinton Hill40.684009-73.967850Entire home/apt95.0300NaNNaN1370NaN
377611193005542763264121Skyline Views from MSG Penthouse559434422LuisManhattanChelsea40.751493-73.996157Entire home/apt599.0300NaNNaN12690NaN
377621193016130087171760Stunning designer Chelsea studio on the best block35491667NatManhattanChelsea40.741720-74.002750Entire home/apt75.0300NaNNaN802420NaN
377631193068036013405081Sunny & Spacious in Queens20380663AnastasiosQueensElmhurst40.731090-73.878070Entire home/apt96.0300NaNNaN23650NaN
377641193086058992851452Bright 2 Bedroom in Astoria910709IsraelQueensDitmars Steinway40.776915-73.907386Entire home/apt225.0300NaNNaN1330NaN